Metalloenzyme binding affinity prediction with VM2

项目概述：据估计，40%到50%的已知酶可以被表征为金属酶， 虽然目前美国FDA批准的药物中只有7%针对这类蛋白质。这是尽管 事实上，有许多已经确定的金属酶靶标参与了几乎每一个 治疗领域，包括抗炎、抗生素、抗病毒药物、抗癌药物等。这是一个很大的问题 部分是因为已经非常困难的药物设计要求保持/增加初始的效力 配体(类药物分子)在改善/保持其靶向选择性和药代动力学性质的同时， 金属配基和金属蛋白质的复杂且往往非直观的性质使之更加困难 互动。因此，对金属酶-配体结合亲和力的准确分子建模预测将 在制药行业的药物研发项目中具有很强的影响力，因为他们将 允许研发科学家进行计算实验，大幅减少昂贵和 克服金属酶抑制剂设计困难所需的耗时的台架实验 他们面临的挑战。然而，目前可用的分子建模方法无法使 足够可靠的预测足以做到这一点。在许多情况下，停靠和评分方法能够确定 抑制剂在金属酶活性部位的姿势，但它们不能正确地按以下顺序对候选抑制剂进行排序 由于它们在其能量模型中缺乏所需的细节，因此它们具有结合亲和力。最近，基于自由能的方法已经 为许多非金属蛋白-配体系列提供可靠的结合亲和力预测 因此，可以帮助加快这些系统的配体发现工作。它们不能提供良好的装订 金属酶-配体体系的亲和力，因为到目前为止，它们都完全基于经典 力场，这从根本上限制了它们对金属配体和金属蛋白质的描述的准确性 互动。这在一定程度上是由于没有包括重要的极化和电荷转移效应，但它 也是因为金属经常表现出的复杂电子结构本质上是量子力学的。 这一快速通道SBIR提案将通过开发一种新的独特的分子建模软件来解决这一问题 工具名为Mzyme-QM-VM2，它将可靠地提供准确的金属酶结合自由能- 统计力学和高可伸缩量子化学的新组合形成缓蚀剂复合体 方法：研究方法。该软件将基于采矿最小自由能计算方法，并将 作为VeraChem的VM2免费能源软件平台的扩展而开发。

Project summary: It is estimated that 40 to 50% of known enzymes can be characterized as metalloenzymes, while currently only 7% of FDA-approved drugs in the United States target this class of protein. This is despite the fact that there are many dozens of already identified metalloenzyme targets involved in virtually every therapeutic area, including anti-inflammatory, antibiotics, antivirals, anticancer drugs, and more. This is in large part because the already very difficult drug design requirement to maintain/increase the potency of an initial ligand (drug-like molecule) while improving/maintaining its target selectivity and pharmacokinetic properties, is made even harder by the complicated and often non-intuitive nature of metal-ligand and metal-protein interactions. Accurate molecular modeling predictions of metalloenzyme-ligand binding affinities, then, would be highly impactful in pharmaceutical industry drug research and development programs, because they would allow R&D scientists to carry out computational experiments drastically reducing the number of expensive and time-consuming bench experiments required to overcome the difficult metalloenzyme inhibitor design challenges they face. However, currently available molecular modeling approaches are unable to make predictions reliable enough to do this. Docking and scoring methods are able to determine, in many cases, the pose of inhibitors in metalloenzyme active sites, but they cannot correctly rank candidate inhibitors in order of binding affinity as they lack the required detail in their energy models. Recently, free energy-based methods have advanced to the point of providing reliable binding affinity predictions for many non-metal protein-ligand series and can, therefore, help speed ligand discovery efforts for these systems. They cannot provide good binding affinities for metalloenzyme-ligand systems though, because to-date they are all entirely based on classical forcefields, which fundamentally limits the accuracy of their descriptions of metal-ligand and metal-protein interactions. This is due, in part, to lack of inclusion of important polarization and charge transfer effects, but it is also because the complex electronic structure, which metals often exhibit, is intrinsically quantum mechanical. This fast-track SBIR proposal will address this by developing a new and unique molecular modeling software tool called Mzyme-QM-VM2, which will provide reliably accurate binding free energies for metalloenzyme- inhibitor complexes by a novel combination of statistical mechanics and highly scalable quantum chemistry methods. This software will be based on mining minima free energy calculation methodology and will be developed as an extension of VeraChem's VM2 free energy software platform.